| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Explain basic concepts of the articial Intelligence and expert systems.
- Explain concepts of uncertainty.
- Solve the engineering problems using hybrid expert systems .
- Solve the engineering problems using fuzzy expert systems.
- Solve the engineering problems using frame-based expert systems.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Concept of Artificial Intelligence, properties, basic elements. Data representation using symbolic logic, verification and structuring. Organization of information and expert systems. Elements of decision making process and decision support systems. Computer applications. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Expert systems: understanding. Basic concept and structure of ES. |
|
2nd Week |
Knowledge representaton. Representation via rule-based systems. |
|
3rd Week |
Knowledge representation. Implementation rules. |
|
4th Week |
Knowledge acquisition. Examples of knowledge acquisition. |
|
5th Week |
The inference engine. The forward chaining algorithm. |
|
6th Week |
The inference engine. The backward chaining algorithm. |
|
7th Week |
Inexact reasoning. Uncertainty management in expert systems. |
|
8th Week |
Midterm |
|
9th Week |
Validity of knowledge base. Examples of checking. |
|
10th Week |
Hybrid expert systems. Practical considerations. |
|
11th Week |
Fuzzy expert systems. Fuzzy rules and inference. |
|
12th Week |
Frame-based expert systems. Designing issues. |
|
13th Week |
Knowledge engineering. Problem assessment, design and testing |
|
14th Week |
Knowledge-based system projects. |
|
| Recommended or Required Reading | James P. Ignizio. Introduction to Expert Systems. The Development and Implementation of Rule-Based Expert Systems, McGraw-Hill, Inc., 1991.
Michael Negnevitsky. A Guide to Intelligent Systems, Addison-Wesley, 2001 |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Problem Solving
- Practice
- Presentation
- Experiment
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
14 |
196 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
25 |
25 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
1 |
14 |
14 |
| Total Work Load | 306 |
| Total Work Load / 30 (h) | 10.2 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
